Ella Louise Stern
April 30, 2023
A h/t to Sharon Machlis for turning people on to the Mapview package, and her excellent walkthroughs of it.
For the data tonight we’ll be using the tidycensus package to pull both census data, as well as geospatial boundaries. Let’s quickly review how we use it.
# uncomment to run, then recomment it out so you don't run it every time
census_api_key("2a6f8c21a30d3024e038d67d7d4eba647dc79cd4", install=TRUE, overwrite=TRUE)Your original .Renviron will be backed up and stored in your R HOME directory if needed.Your API key has been stored in your .Renviron and can be accessed by Sys.getenv("CENSUS_API_KEY").
To use now, restart R or run `readRenviron("~/.Renviron")`[1] "2a6f8c21a30d3024e038d67d7d4eba647dc79cd4"#pull for MI counties
mi_counties_withgeo <- get_acs(geography = "county",
variables = c(myvars),
state = "MI",
output = "wide",
geometry = TRUE)Getting data from the 2017-2021 5-year ACSDownloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
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|======================================================================| 100%#all counties in the US
all_counties_withgeo <- get_acs(geography = "county",
variables = c(myvars),
output = "wide",
geometry = TRUE)Getting data from the 2017-2021 5-year ACSDownloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.Simple feature collection with 3221 features and 8 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -179.1489 ymin: 17.88328 xmax: 179.7785 ymax: 71.36516
Geodetic CRS: NAD83
First 10 features:
GEOID NAME totalpopE totalpopM medincomeE
1 20161 Riley County, Kansas 72602 NA 53296
2 19159 Ringgold County, Iowa 4739 NA 57700
3 30009 Carbon County, Montana 10488 NA 63178
4 16007 Bear Lake County, Idaho 6327 NA 60337
5 55011 Buffalo County, Wisconsin 13314 NA 61167
6 31185 York County, Nebraska 14164 NA 66337
7 08037 Eagle County, Colorado 55693 NA 91338
8 42129 Westmoreland County, Pennsylvania 355107 NA 64708
9 40079 Le Flore County, Oklahoma 48436 NA 43049
10 48053 Burnet County, Texas 48424 NA 65363
medincomeM medageE medageM geometry
1 2489 25.5 0.1 MULTIPOLYGON (((-96.96095 3...
2 5058 44.3 1.0 MULTIPOLYGON (((-94.47167 4...
3 4261 50.7 0.9 MULTIPOLYGON (((-109.7987 4...
4 7039 38.9 1.1 MULTIPOLYGON (((-111.6345 4...
5 2352 46.5 0.5 MULTIPOLYGON (((-92.08384 4...
6 4128 39.5 1.2 MULTIPOLYGON (((-97.82629 4...
7 4058 37.8 0.8 MULTIPOLYGON (((-107.1137 3...
8 1350 47.1 0.2 MULTIPOLYGON (((-79.90487 4...
9 1869 38.8 0.3 MULTIPOLYGON (((-95.05996 3...
10 4694 44.7 0.3 MULTIPOLYGON (((-98.45924 3...#remove MOE columns - they all end with "M"
mi_counties_withgeo <- mi_counties_withgeo %>%
select(-ends_with("M"))
mi_counties_withgeoSimple feature collection with 83 features and 5 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -90.41814 ymin: 41.69612 xmax: -82.41347 ymax: 48.2388
Geodetic CRS: NAD83
First 10 features:
GEOID NAME totalpopE medincomeE medageE
1 26119 Montmorency County, Michigan 9164 43913 56.4
2 26147 St. Clair County, Michigan 160067 62847 44.1
3 26053 Gogebic County, Michigan 14858 41948 50.2
4 26047 Emmet County, Michigan 33946 63488 45.6
5 26103 Marquette County, Michigan 66380 57981 39.2
6 26055 Grand Traverse County, Michigan 94562 69393 42.9
7 26141 Presque Isle County, Michigan 12976 52181 55.8
8 26137 Otsego County, Michigan 25016 59330 44.6
9 26003 Alger County, Michigan 8911 48822 49.3
10 26115 Monroe County, Michigan 154068 68006 42.8
geometry
1 MULTIPOLYGON (((-84.37166 4...
2 MULTIPOLYGON (((-82.99626 4...
3 MULTIPOLYGON (((-90.41814 4...
4 MULTIPOLYGON (((-85.03905 4...
5 MULTIPOLYGON (((-87.40383 4...
6 MULTIPOLYGON (((-85.58321 4...
7 MULTIPOLYGON (((-84.25202 4...
8 MULTIPOLYGON (((-84.85745 4...
9 MULTIPOLYGON (((-86.70904 4...
10 MULTIPOLYGON (((-83.77392 4...#remove that trailing "E"
colnames(mi_counties_withgeo) <- sub("E$", "", colnames(mi_counties_withgeo)) # $ means end of string only
mi_counties_withgeoSimple feature collection with 83 features and 5 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -90.41814 ymin: 41.69612 xmax: -82.41347 ymax: 48.2388
Geodetic CRS: NAD83
First 10 features:
GEOID NAM totalpop medincome medage
1 26119 Montmorency County, Michigan 9164 43913 56.4
2 26147 St. Clair County, Michigan 160067 62847 44.1
3 26053 Gogebic County, Michigan 14858 41948 50.2
4 26047 Emmet County, Michigan 33946 63488 45.6
5 26103 Marquette County, Michigan 66380 57981 39.2
6 26055 Grand Traverse County, Michigan 94562 69393 42.9
7 26141 Presque Isle County, Michigan 12976 52181 55.8
8 26137 Otsego County, Michigan 25016 59330 44.6
9 26003 Alger County, Michigan 8911 48822 49.3
10 26115 Monroe County, Michigan 154068 68006 42.8
geometry
1 MULTIPOLYGON (((-84.37166 4...
2 MULTIPOLYGON (((-82.99626 4...
3 MULTIPOLYGON (((-90.41814 4...
4 MULTIPOLYGON (((-85.03905 4...
5 MULTIPOLYGON (((-87.40383 4...
6 MULTIPOLYGON (((-85.58321 4...
7 MULTIPOLYGON (((-84.25202 4...
8 MULTIPOLYGON (((-84.85745 4...
9 MULTIPOLYGON (((-86.70904 4...
10 MULTIPOLYGON (((-83.77392 4...Mapping Michigan counties with mapview
Customize colors
mapview(mi_counties_withgeo, zcol = "medincome",
col.regions = RColorBrewer::brewer.pal(9, "Greens"),
alpha.regions = 1)Warning: Found less unique colors (9) than unique zcol values (82)!
Interpolating color vector to match number of zcol values.This map’s dark background appeared automatically, because mapview determined the map included a lot of light colors. You can turn off that feature.
mapview(mi_counties_withgeo, zcol = "medincome",
col.regions = RColorBrewer::brewer.pal(9, "Greens"),
alpha.regions = 1)Warning: Found less unique colors (9) than unique zcol values (82)!
Interpolating color vector to match number of zcol values.Two maps together
map_income <- mapview(mi_counties_withgeo, zcol = "medincome",
col.regions = RColorBrewer::brewer.pal(9, "Greens"),
alpha.regions = 1)Warning: Found less unique colors (9) than unique zcol values (82)!
Interpolating color vector to match number of zcol values.map_age <- mapview(mi_counties_withgeo, zcol = "medage",
col.regions = RColorBrewer::brewer.pal(9, "Greens"),
alpha.regions = 1)Warning: Found less unique colors (9) than unique zcol values (74)!
Interpolating color vector to match number of zcol values.Side-by-side slider to compare the two, from the leaflet.extras2 package
To turn off legends, hover text, popups
mapview(mi_counties_withgeo, zcol = "medincome",
col.regions = RColorBrewer::brewer.pal(9, "Greens"),
alpha.regions = 1,
legend = FALSE,
label = FALSE,
popup = FALSE)Warning: Found less unique colors (9) than unique zcol values (82)!
Interpolating color vector to match number of zcol values.Customize labels
mylabel <- glue::glue("{mi_counties_withgeo$NAM} {mi_counties_withgeo$medincome}")
mapview(mi_counties_withgeo, zcol = "medincome",
col.regions = RColorBrewer::brewer.pal(9, "Greens"),
alpha.regions = 1,
label = mylabel)Warning: Found less unique colors (9) than unique zcol values (82)!
Interpolating color vector to match number of zcol values.Customize popups
mypopup <- glue::glue("<strong>{mi_counties_withgeo$NAM}</strong><br />
Total Population: {mi_counties_withgeo$totalpop}<br />
Median Income: {mi_counties_withgeo$medincome}") %>%
lapply(htmltools::HTML)
mylabel <- glue::glue("{mi_counties_withgeo$State} {mi_counties_withgeo$PctChange10_20}%") %>%
lapply(htmltools::HTML)[[1]]
<strong>Montmorency County, Michigan</strong><br />
Total Population: 9164<br />
Median Income: 43913
[[2]]
<strong>St. Clair County, Michigan</strong><br />
Total Population: 160067<br />
Median Income: 62847
[[3]]
<strong>Gogebic County, Michigan</strong><br />
Total Population: 14858<br />
Median Income: 41948
[[4]]
<strong>Emmet County, Michigan</strong><br />
Total Population: 33946<br />
Median Income: 63488
[[5]]
<strong>Marquette County, Michigan</strong><br />
Total Population: 66380<br />
Median Income: 57981
[[6]]
<strong>Grand Traverse County, Michigan</strong><br />
Total Population: 94562<br />
Median Income: 69393mapview(mi_counties_withgeo, zcol = "medincome",
col.regions = RColorBrewer::brewer.pal(9, "Greens"),
alpha.regions = 1,
popup = mypopup)Warning: Found less unique colors (9) than unique zcol values (82)!
Interpolating color vector to match number of zcol values.